Big Data Intro for IT Administrators, Devs and Consultants
Big Data Intro for IT Administrators, Devs and Consultants, available at $19.99, has an average rating of 4, with 16 lectures, based on 15 reviews, and has 292 subscribers.
You will learn about Grasp why "Big Data" is the current Gold Rush for Developers / Consultants and Admins Understand the Hadoop ECO System. Basic HDFS / YARN / HIVE / SCOOP / SPARK will be covered with examples This course is ideal for individuals who are Software engineers who want to expand their skills into the world of distributed computing or System Engineers that want to expand their skillsets beyond the single server or Developers who want to write and develop distributed systems or Database Administrators who want to manage an Hadoop ODS or EDW with HIVE It is particularly useful for Software engineers who want to expand their skills into the world of distributed computing or System Engineers that want to expand their skillsets beyond the single server or Developers who want to write and develop distributed systems or Database Administrators who want to manage an Hadoop ODS or EDW with HIVE.
Enroll now: Big Data Intro for IT Administrators, Devs and Consultants
Summary
Title: Big Data Intro for IT Administrators, Devs and Consultants
Price: $19.99
Average Rating: 4
Number of Lectures: 16
Number of Published Lectures: 16
Number of Curriculum Items: 16
Number of Published Curriculum Objects: 16
Original Price: £89.99
Quality Status: approved
Status: Live
What You Will Learn
- Grasp why "Big Data" is the current Gold Rush for Developers / Consultants and Admins
- Understand the Hadoop ECO System.
- Basic HDFS / YARN / HIVE / SCOOP / SPARK will be covered with examples
Who Should Attend
- Software engineers who want to expand their skills into the world of distributed computing
- System Engineers that want to expand their skillsets beyond the single server
- Developers who want to write and develop distributed systems
- Database Administrators who want to manage an Hadoop ODS or EDW with HIVE
Target Audiences
- Software engineers who want to expand their skills into the world of distributed computing
- System Engineers that want to expand their skillsets beyond the single server
- Developers who want to write and develop distributed systems
- Database Administrators who want to manage an Hadoop ODS or EDW with HIVE
Understand “Big Data” and grasp why, if you are a Developer, Database Administrator, Software Architect or a IT Consultant, why you should be looking at this technology stack
There are more job opportunities in Big Data management and Analytics than there were last year and many IT professionals are prepared to invest time and money for the training.
Why Is Big Data Different?
In the old days… you know… a few
years ago, we would utilize systems to extract, transform and load data
(ETL) into giant data warehouses that had business intelligence
solutions built over them for reporting. Periodically, all the systems
would backup and combine the data into a database where reports could be
run and everyone could get insight into what was going on.
The
problem was that the database technology simply couldn’t handle
multiple, continuous streams of data. It couldn’t handle the volume of
data. It couldn’t modify the incoming data in real-time. And reporting
tools were lacking that couldn’t handle anything but a relational query
on the back-end. Big Data solutions offer cloud hosting, highly indexed
and optimized data structures, automatic archival and extraction
capabilities, and reporting interfaces have been designed to provide
more accurate analyses that enable businesses to make better decisions.
Better
business decisions means that companies can reduce the risk of their
decisions, and make better decisions that reduce costs and increase
marketing and sales effectiveness.
What Are the Benefits of Big Data?
This infographic from Informatica walks through the risks and opportunities associated with leveraging big data in corporations.
Big Data is Timely – A large percentage of each workday, knowledge workers spend attempting to find and manage data.
Big Data is Accessible – Senior executives report that accessing the right data is difficult.
Big Data is Holistic – Information is currently kept in silos within the organization. Marketing data, for example, might be found in web analytics, mobile analytics, social analytics, CRMs, A/B Testing tools, email marketing systems, and more… each with focus on its silo.
Big Data is Trustworthy – Organizations measure the monetary cost of poor data quality. Things as simple as monitoring multiple systems for customer contact information updates can save millions of dollars.
Big Data is Relevant – Organizations are dissatisfied with their tools ability to filter out irrelevant data. Something as simple as filtering customers from your web analytics can provide a ton of insight into your acquisition efforts.
Big Data is Authoritive – Organizations struggle with multiple versions of the truth depending on the source of their data. By combining multiple, vetted sources, more companies can produce highly accurate intelligence sources.
Big Data is Actionable – Outdated or bad data results in organizations making bad decisions that can cost billions.
.
Here I present a curriculum as to the current state of my Cloudera courses.
My Hadoop courses are based on Vagrant so that you can practice and
destroy your virtual environment before applying the installation onto
real servers/VMs.
.
For those with little or no knowledge of the Hadoop eco system
Udemy course : Big Data Intro for IT Administrators, Devs and Consultants
.
I would first practice with Vagrant so that you can carve out a
virtual environment on your local desktop. You don’t want to corrupt
your physical servers if you do not understand the steps or make a
mistake.
Udemy course : Real World Vagrant For Distributed Computing
.
I would then, on the virtual servers, deploy Cloudera Manager plus
agents. Agents are the guys that will sit on all the slave nodes ready
to deploy your Hadoop services
Udemy course : Real World Vagrant – Automate a Cloudera Manager Build
.
Then deploy the Hadoop services across your cluster (via the
installed Cloudera Manager in the previous step). We look at the logic
regarding the placement of master and slave services.
Udemy course : Real World Hadoop – Deploying Hadoop with Cloudera Manager
.
If you want to play around with HDFS commands (Hands on distributed file manipulation).
Udemy course : Real World Hadoop – Hands on Enterprise Distributed Storage.
.
You can also automate the deployment of the Hadoop services via
Python (using the Cloudera Manager Python API). But this is an advanced
step and thus I would make sure that you understand how to manually
deploy the Hadoop services first.
Udemy course : Real World Hadoop – Automating Hadoop install with Python!
.
There is also the upgrade step. Once you have a running cluster, how
do you upgrade to a newer hadoop cluster (Both for Cloudera Manager and
the Hadoop Services).
Udemy course : Real World Hadoop – Upgrade Cloudera and Hadoop hands on
Course Curriculum
Chapter 1: As a Developer, Administrator or Architect – Why should you consider "Big Data"
Lecture 1: As a Developer, Administrator or Architect – Why should you consider "Big Data"
Lecture 2: Suggested course curriculum to follow …
Chapter 2: Whiteboarding Sessions
Lecture 1: Whiteboarding the rational
Lecture 2: Part I – Whiteboarding some of the Hadoop Services
Lecture 3: Part II – Whiteboarding some of the Hadoop Services
Lecture 4: Part III – Whiteboarding some of the Hadoop Services
Chapter 3: Enterprise Examples
Lecture 1: We step through an example RDBMS system
Lecture 2: Hadoop Distributors – apache.org, Cloudera, Hortonworks and MapR
Lecture 3: Hadoop Cloud Operators – Amazon EMR and Microsoft Azure
Chapter 4: Hands on Hadoop Services
Lecture 1: Operating a Local Hadoop Installment
Lecture 2: SQOOP SERVICE – Move data from the database into Hadoop
Lecture 3: HIVE SERVICE – We apply sql statements within Hadoop on the copied data.
Lecture 4: HIVE SERVICE II – We apply sql statements within Hadoop on the copied data.
Lecture 5: HDFS SERVICE – We move some files into HDFS, ready for SPARK processing
Lecture 6: SPARK SERVICE – We perform data analytics based on the data copied into HDFS
Chapter 5: Conclusion
Lecture 1: Conclusion
Instructors
-
Toyin Akin
Big Data Engineer, Capital Markets FinTech Developer
Rating Distribution
- 1 stars: 1 votes
- 2 stars: 1 votes
- 3 stars: 3 votes
- 4 stars: 3 votes
- 5 stars: 7 votes
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